A continuous binning for discrete, sparse and concentrated observations.

ABSTRACT: Discrete observations from data which are obtained from sparse, and yet concentrated events are often observed (e.g. road accidents or murders). Traditional methods to compute summary statistics often include placing the data in discrete bins but for this type of data this approach often results in large numbers of empty bins for which no function or summary statistic can be computed. Here, a method for dealing with sparse and concentrated observations is constructed, based on a sequence of non-overlapping bins of varying size, which gives a continuous interpolation of data for computing summary statistics of the values for the data, such as the mean. The method presented here overcomes the problem which sparsity and concentration present when computing functions to represent the data. Imp

SUBMITTER: Prieto Curiel R 

PROVIDER: S-EPMC6994295 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature